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Enhancing encrypted traffic analysis via source APIs: A robust approach for malicious traffic detection

Computers and Security 2025年 156卷

作者： Lin, Wanshuang Xia, Chunhe Wang, Tianbo Liu, Mengyao Li, Yang School of Cyber Science and Technology Beihang University Beijing100191 China Key Laboratory of Beijing Network Technology Beihang University Beijing100191 China Guangxi Collaborative Innovation Center of MultiSource Information Integration and Intelligent Processing Guangxi Normal University Guilin541004 China

The widespread adoption of encryption protocols has increased the complexity of detecting malicious Android traffic. By randomizing payload content, encryption obscures semantically explicit features in network traffic, thereby concealing its behavioral intent. Although existing methods mitigate this issue by expanding feature sets or extracting spatiotemporal patterns, they do not fundamentally reconstruct the original payload semantics. In this paper, we propose RATD, a detection model that enhances encrypted traffic representation by introducing semantics of source-APIs. This approach leverages the correlation between system API calls made prior to traffic transmission (referred to as source APIs) and the behavioral intent within encrypted traffic, thereby compensating for semantic loss. First, we construct API-traffic association samples by monitoring network connection APIs. Then, we transform the API sequences into graphs and apply a Graph Convolutional network (GCN) to learn their structural and semantic representations. These features are fused with corresponding traffic features through a multi-source encoder module. Finally, to address the challenges of limited data availability in real-world deployment, we introduce a representation enhancement module to improve model's robustness in scenarios with missing data. Experimental results show that RATD is significantly better than the state-of-the-art models across multiple datasets. In particular, in scenarios with missing API data, the accuracy of our model decreases by at most 2.9%, showing a stronger environmental adaptability. © 2025

关键词： HTTP

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A hybrid artificial fish-school optimization algorithm for solving the quadratic assignment problem 10

A hybrid artificial fish-school optimization algorithm for s...

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2014 10th International Conference on Natural Computation, ICNC 2014

作者： Ziqiang, Li Yi, Yang Yang, Qiwei School of Information and Engineering Xiangtan University Xiangtan411105 China Key Laboratory of Intelligent Computing Information Processing of Ministry of Education Xiangtan411105 China

ISBN: (纸本)9781479951505

The quadratic assignment problem (QAP) is a classic combinatorial optimization problem, which is of the NP-hard nature. In this paper, a hybrid artificial fish school optimization algorithm (HAFSOA) is proposed. In HAFSOA, the heuristic information is used in constructing some better initial individuals and its search ability of the global optimal solution is improved by a combination of the modified fish school optimization and differential evolution. In addition, by taking different visual distances for three behaviors: preying, clustering and following, the convergence speed of the proposed HAFSOA is speeded up. Many QAP experimental results show that the proposed HAFSOA can solve QAP better. © 2014 IEEE.

关键词： Evolutionary algorithms

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A channel allocation algorithm based on demand in cognitive wireless mesh networks

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2012 National Conference on information Technology and Computer Science, CITCS 2012

作者： Pei, Tingrui Li, Jing Li, Zhetao College of Information Engineering Xiangtan University Xiangtan 411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministery of Education Xiangtan University China

ISBN: (纸本)9789491216381

We proposed a channel allocation algorithm, which was a modification of graph theory and based on demand to optimize the performance of cognitive wireless Mesh network. The key idea of the algorithm was to design a proportion coefficient of actual flow to demand flow, and then cognitive users with the prior proportion could access their optimal channels. The results of simulation show that the proposed algorithm improves the throughput and fairness of network. © 2012. The authors - Published by Atlantis Press.

关键词： Cognitive radio

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A heuristic particle swarm optimization with quasi-human strategy for weighted circles packing problem

A heuristic particle swarm optimization with quasi-human str...

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2012 8th International Conference on Natural Computation, ICNC 2012

作者： Li, Ziqiang Xie, Yanfang Tian, Zuijun Zhou Lichao School of Information and Engineering Xiangtan University Xiangtan 411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan 411105 China

ISBN: (纸本)9781457721311

The weighted circles layout problem belongs to the layout optimization problem with performance constraints. Due to its NP-hard property, it is difficult to solve in polynomial time. In this paper, a heuristic particle swarm optimization approach with quasi-human strategy (HQHPSA) is presented for this problem. Its layout scheme is constructed through the proposed heuristic method: that both circular radius and the norm of row vector of the matrix and sub-vector are taken as the probability factors of the roulette selection and the circles are located by arranging round existing circles in peripheral with counterclockwise. The complexity of the proposed heuristic method is only O(n) for one layout scheme. The better layout solution obtained through the proposed heuristic method is taken as the elite particle individual. The PSO with quasi-human strategy is used to optimize the elite particle into the optimal solution. The Numerical experiments show that the performance of proposed algorithm is superior to the existing algorithms. © 2012 IEEE.

关键词： Heuristic methods

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Mt tuning on red: A dependency-based evaluation metric 10

Mt tuning on red: A dependency-based evaluation metric

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10th Workshop on Statistical Machine Translation, WMT 2015 at the 2015 Conference on Empirical Methods in Natural Language processing, EMNLP 2015

作者： Li, Liangyou Yu, Hui Liu, Qun Adapt Centre School of Computing Dublin City University Ireland Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781941643327

In this paper, we describe our submission to WMT 2015 Tuning Task. We integrate a dependency-based MT evaluation metric, RED, to Moses and compare it with BLEU and METEOR in conjunction with two tuning methods: MERT and MIRA. Experiments are conducted using hierarchical phrase-based models on Czech-English and English-Czech tasks. Our results show that MIRA performs better than MERT in most cases. Using RED performs similarly to METEOR when tuning is performed using MIRA. We submit our system tuned by MIRA towards RED to WMT 2015. In human evaluations, we achieve the 1st rank in all 7 systems on the English-Czech task and 6/9 on the Czech-English task. © EMNLP 2015. All rights reserved.

关键词： Natural language processing systems

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Uncer2Natural: Uncertainty-Aware Unsupervised Image Denoising 48

Uncer2Natural: Uncertainty-Aware Unsupervised Image Denoisin...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Huang, Chenyu Tan, Weimin Shi, Jiaxing Xing, Zhen Yan, Bo Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

ISBN: (纸本)9781728163277

Recently, unsupervised image denoising methods learning from paired noisy samples have received increasing attention. These methods build on the idea that the mean of multiple noisy images of the same scene is the ideal clean image. However, these methods ignore the effect of Aleatoric uncertainty in the noisy image (e.g., pixels deviating from the expected distribution). The presence of Aleatoric uncertainty causes degradation of the reconstructed target pixels, resulting in high uncertainty for these pixels (i.e., low confidence), which in turn leads to sub-optimal denoising results. To address this problem, we propose a novel uncertainty-aware unsupervised image denoising method named Uncer2Natural (U2N). It dynamically predicts the Aleatoric uncertainty for each noisy sample and produces satisfactory denoising results by reducing the effect of Aleatoric uncertainty. Extensive experimental results show that U2N outperforms state-of-the- art unsupervised image denoising methods in terms of both quantitative metrics and qualitative visual quality. © 2023 IEEE.

关键词： Pixels

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Non-Destructive Judgment of Watermelon Ripeness Based on Multi-Feature Fusion of Image 20

Non-Destructive Judgment of Watermelon Ripeness Based on Mul...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Shi, Hongbao Liu, Jun Huang, Yimei Li, Jinping School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China

ISBN: (纸本)9798350356328

The nondestructive judgment of watermelon ripeness is a key issue in everyday life. Whether watermelons are ripe or not affects the interests of watermelon farmers and consumers. So far, acoustic, electrical, machine vision and other methods have been applied to the nondestructive judgment of agricultural product ripeness, but the equipment used in the lab, such as spectrometers and laser Doppler vibrometers, is expensive, difficult to carry, and has limited practical value in daily life. Based on practical experience, the ripeness of watermelons can be judged by observing specific physical characteristics, such as the rind's glossiness, texture, fuzz on the fruit stalk and navel. In this study, images of the watermelon's texture, fuzz and navel were captured using a smartphone. We utilized ResN et with an embedded Coordinate Attention mechanism and Swin-Transformer networks to independently analyze the ripeness of each feature. Then we combined the output prediction scores using post-fusion technology, with the highest weighted prediction score judging the final ripeness assessment. Our method achieved a 90.2% accuracy rate on the test set. The experiments demonstrate that a deep learning model utilizing multi-feature fusion of image data can reliably and accurately identify watermelon ripeness, even under varying image conditions. © 2024 IEEE.

关键词： Laser Doppler velocimeters

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CASICT-DCU neural machine translation systems for WMT17 2

CASICT-DCU neural machine translation systems for WMT17

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2nd Conference on Machine Translation, WMT 2017

作者： Zhang, Jinchao Porkaew, Peerachet Hu, Jiawei Zhao, Qiuye Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China ADAPT Centre School of Computing Dublin City University Ireland

ISBN: (纸本)9781945626968

We participated in the WMT 2016 shared news translation task on English ? Chinese language pair. Our systems are based on the encoder-decoder neural machine translation model with the attention mechanism. We employ the Gated Recurrent Unit (GRU) with the linear associative connection to build deep encoder and address the unknown words with the dictionary replace approach. The dictionaries are extracted from the parallel training data with unsupervised word alignment method. In the decoding procedure, the translation probabilities of the target word from different models are averagely combined as the ensemble strategy. In this paper, we introduce our systems from data preprocessing to post-editing in details. © 2017 Association for Computational Linguistics

关键词： Neural machine translation

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Color Correction for Stereoscopic Images Based on Gradient Preservation

Color Correction for Stereoscopic Images Based on Gradient P...

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computing Conference, 2019

作者： Liu, Pengyu Niu, Yuzhen Chen, Junhao Shi, Yiqing College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Lab of the Network Computing and Intelligent Information Processing Fujian China

ISBN: (纸本)9783030228705

Color correction can eliminate the color difference between similar images in image stitching and 3D video reconstruction. The result images generated by the local color correction algorithms usually show structure inconsistency problem with the input images. In order to solve this problem, we propose a structure consistent color correction algorithm for stereoscopic images based on gradient preservation. This method can not only eliminate color difference between reference and target images, but also optimize structure between the input target image and the result image. Firstly, the algorithm extracts the structure information of the target image and style information of the reference image using the SIFT algorithm and generates the structure image and the pixel matching image. Then an initial result image is generated by local pixel mapping. Finally the initial result image is iteratively optimized by the gradient preserving algorithm. The experimental results show that our algorithm can not only optimize the structure inconsistency, but also effectively process image pairs with large color difference. © 2019, Springer Nature Switzerland AG.

关键词： Mapping

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Haze weather recognition based on multiple features and Random Forest

Haze weather recognition based on multiple features and Rand...

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2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018

作者： Fan, Xue Feng, Zhiquan Yang, Xiaohui Xu, Tao Tian, Jinglan Lv, Na School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China

ISBN: (纸本)9781728105512

A single image based haze weather recognition is the fundamental operation of the applications of outdoor computer vision. Currently, the recognition results are remains undesirable and most existing methods are only for the fixed scene. In this paper, we propose multiple features and Random Forest based haze weather classification method for any scenario to improve the detection accuracy. First, through systematically investigation, multiple features are extracted and properly processed. Then, these features are combined into high dimension vectors and the Random Forest is adopted to lean an adaptive classifier for haze recognition. In the experiment, an outdoor image set which contains around 4000 images is collected. Form the experimental results is can be seen that the proposed method achieves 97.4% recognition accuracy of the haze weather on the collected dataset. © 2018 IEEE.

关键词： Decision trees

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